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Review AI-generated code as if a teammate wrote it—but verify it more deliberately. Read the complete diff, compare it with the actual acceptance criteria, run the repository’s normal checks independently, and add tests for negative and boundary behavior. Treat the agent’s tests, dependency choices, security claims, and summary as untrusted artifacts that require the same scrutiny as production code. A human must understand, validate, and approve the change before merge or release; a green test run is evidence, not proof of correctness.
Why does AI-generated code still require human approval?
An AI coding agent can produce an implementation, tests, documentation, and a plausible explanation without demonstrating that the change satisfies the real requirement. The person approving the pull request remains responsible for behavior, security, maintenance, and operational consequences. The UK Home Office engineering standard states that “AI-assisted outputs MUST be reviewed and approved by a human before reaching production” and that teams “will retain full accountability for all AI-assisted code and outputs” (Use AI – Engineering Guidance and Standards, updated 20 March 2026). That is a departmental policy example, not a universal law, but it expresses the appropriate engineering baseline.
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Use the same coding, testing, and security standards you would apply to human-written code. Do not accept an agent’s summary as a substitute for examining the files, and do not assume generated tests are independent simply because they pass. Your review should answer two separate questions:
- Does this patch implement the requested behavior, including failure and edge cases?
- Is the resulting code safe, maintainable, and compatible with the repository’s architecture and operating environment?
Those questions require evidence from the diff, tools, tests, and reasoning—not confidence in how fluently the agent describes its work.
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How do I define what the patch is supposed to do?
Start with the intended outcome before reading implementation details. Turn the issue, ticket, or request into observable acceptance criteria and identify interfaces, data, and security constraints that the patch must preserve.
Build a review brief
- Write the expected behavior. State inputs, outputs, side effects, error responses, and compatibility requirements in concrete terms.
- List affected surfaces. Include public APIs, command-line behavior, database schemas, background jobs, user interfaces, deployment files, and monitoring.
- Identify edge conditions. Consider empty, oversized, malformed, duplicated, expired, concurrent, unauthorized, and partially unavailable inputs where they apply.
- Read repository conventions. Check architecture documents, contribution instructions, existing error-handling patterns, supported runtime versions, and the normal build and test commands.
- Mark security requirements. Note authentication and authorization rules, data classification, trust boundaries, logging restrictions, and regulatory obligations relevant to the change.
GitHub’s guidance on reviewing AI-generated code recommends checking whether the implementation fits the project’s intent and conventions, not merely whether it compiles (Review AI-generated code). If the request is ambiguous, resolve the ambiguity with the owner before approval; otherwise record the assumption and test it explicitly.
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How do I inspect the entire AI-generated diff?
Read every changed file, not only the files named in the agent’s summary. Unexpected scope is a review finding in its own right: an apparently small feature can alter CI, dependencies, deployment, permissions, or test behavior.
Use a repeatable diff sequence
- Open the pull request’s complete changed-file list and compare it with the intended scope.
- Read the diff file by file, including context around each hunk. Then open the resulting file where control flow is easier to understand than in a patch.
- Inspect tests for deletion, assertion changes, skipped cases, new mocks, and altered fixtures.
- Review lockfiles, manifest files, generated code, configuration, environment templates, migration scripts, CI workflows, container files, deployment descriptors, and documentation.
- Search for changed permissions, network endpoints, shell commands, deserialization, dynamic evaluation, logging of sensitive values, and new data stores.
- Compare the final tree with the base branch for files that were renamed, moved, or deleted.
For a local branch, commands such as these expose scope and history:
git fetch origin main
git diff --stat origin/main...HEAD
git diff --name-status origin/main...HEAD
git diff --check origin/main...HEAD
git diff origin/main...HEADgit diff --check catches whitespace errors, but it is not a correctness or security test. Use the repository’s own review interface as well, because it shows generated-file markers, deleted files, and CI results that a local command may not make obvious.
Look for scope expansion and hidden behavior
Ask why each unrelated-looking edit exists. An agent may upgrade a package, rewrite a formatter configuration, alter a workflow permission, or change a shared helper while implementing a narrow request. Separate necessary changes into a focused commit or pull request when possible. If an edit cannot be explained from the acceptance criteria, treat it as unapproved scope until the author removes or justifies it.
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How should I review tests written or edited by the agent?
Agent-authored tests are useful evidence, but they are part of the change and can contain the same misunderstandings as the implementation. Judge each test against the requirement and the observable behavior, not against the generated code.
Check test independence
- Does the test exercise the real unit, service, or endpoint, or does a mock replace the behavior under test?
- Are assertions specific enough to fail when the requirement is broken, rather than merely checking that a function returned without throwing?
- Does the fixture represent realistic permissions, data sizes, time zones, encodings, retries, and failure responses?
- Are tests deleted, skipped, marked as expected failures, or changed from exact assertions to broad matchers?
- Does the test assert the requirement, or does it reproduce an implementation detail such as a particular private helper call?
- Would the test still pass if the defect described in the ticket were reintroduced?
Add cases the agent did not author
Derive additional tests from acceptance criteria and threat boundaries. Include at least one invalid input, one boundary value, and one authorization or failure path when relevant. For stateful or asynchronous code, test retries, duplicate requests, cancellation, timeouts, partial completion, and recovery after a dependency fails. For parsers and protocol code, vary encoding, truncation, unknown fields, ordering, and maximum sizes.
OWASP Software Assurance Maturity Model guidance says people remain responsible for defining what should be tested and verifying that generated tests reflect intent (SAMM Guidance for AI-Assisted Development). A test suite can be broad and still miss the one property that matters, so design at least one adversarial case independently of the agent.
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Run the project’s normal build, test, lint, type-checking, and static-analysis workflow from a clean checkout or clean workspace. Compare warnings and failures with the base branch, then add targeted checks based on the acceptance criteria. Do not let the agent choose which checks count as sufficient.
Use layers of verification
| Layer | What it verifies | Reviewer action |
|---|---|---|
| Build and type checks | Compilation, generated artifacts, interfaces, and supported runtime assumptions | Run the documented command and investigate new warnings, not only hard failures |
| Unit tests | Local logic and error handling | Check boundary and negative cases and inspect mocks |
| Integration tests | Real interactions among modules, databases, queues, or external contracts | Exercise serialization, permissions, retries, and transaction behavior |
| End-to-end tests | User-visible workflows across deployed components | Use them where the change crosses process or service boundaries |
| Static analysis | Type, data-flow, code-quality, and known insecure patterns | Compare findings with baseline and triage suppressions |
| Manual exploratory checks | Behavior that automation does not model well | Try realistic workflows, malformed inputs, and recovery paths |
Run the baseline branch through the same commands when practical. A newly introduced warning, flaky test, coverage drop, or changed performance characteristic can matter even when the final command exits successfully. Preserve logs for the pull request so another reviewer can reproduce the decision.
Test the requirement, not the implementation
Suppose a request says a user without permission must receive a forbidden response and no data. A test that only checks an internal authorization helper was called can pass while the endpoint still leaks a response body. Assert the externally visible status, payload, side effects, and audit behavior. Where the requirement is quantitative, encode the threshold and units in the test rather than relying on a comment.
GitHub recommends tests and static analysis as early review checks (Review AI-generated code), while the Australian Cyber Security Centre’s Guidelines for software development describes positive and negative unit and integration testing alongside security testing. Neither source treats a single green run as proof that a change is correct.
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Which security checks belong in an AI-code review?
Choose security verification in proportion to the change’s impact and exposure. A UI-only text change does not need the same work as code that handles credentials, authorization, payments, uploads, network requests, or infrastructure permissions.
Review the main security boundaries
- Authentication: confirm identity checks cannot be bypassed by alternate routes, stale sessions, missing headers, or error paths.
- Authorization: verify object-level and action-level permissions for every affected endpoint, including cross-tenant and unauthenticated requests.
- Input handling: test validation, canonicalization, size limits, encoding, injection resistance, and safe parsing.
- Data handling: check encryption expectations, retention, redaction, log contents, exports, caching, and accidental exposure in exceptions.
- Dependencies and supply chain: inspect new packages, versions, transitive dependencies, licenses, maintenance signals, and integrity controls.
- Operational behavior: examine secrets, network destinations, filesystem access, container privileges, CI tokens, and deployment changes.
Match techniques to risk
| Change characteristic | Useful additional verification |
|---|---|
| Public endpoint or parser | Black-box tests, malformed-input tests, fuzzing, rate-limit checks, and web-application scanning where applicable |
| Authentication, authorization, or sensitive data | Threat modeling, focused negative tests, SAST, secret detection, and peer review by someone familiar with the security model |
| New library or service | Software composition analysis, license review, version and provenance checks, and integration tests against failure modes |
| Infrastructure or CI change | Review workflow permissions, credential scope, artifact handling, deployment plan, and rollback behavior |
| Complex algorithm or untrusted input | Structural tests, property-based tests, fuzzing, resource-limit tests, and historical regression cases |
NIST IR 8397 lists broadly applicable techniques including threat modeling, automated testing, static code scanning, hardcoded-secret detection, built-in protections, black-box and structural tests, historical tests, fuzzing, web-application scanners where applicable, and checks of included libraries, packages, and services (NIST IR 8397, October 2021). The Australian guidance also identifies SAST, DAST, and software composition analysis as complementary measures.
AI can help identify insecure patterns, but validate its findings with appropriately skilled people. A model may flag a harmless construct, miss a business-logic flaw, or recommend an unsafe fix. Treat automated findings as leads that require reproduction, impact analysis, and a documented disposition.
How do I review dependencies and configuration changes?
Open the manifest and lockfile together. Confirm that every new dependency is necessary, supported by the project’s runtime, compatible with its license policy, and pinned or resolved according to repository practice. Check transitive additions and whether the package introduces install scripts, native code, telemetry, broad permissions, or a new network destination.
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Run the repository’s dependency vulnerability and license checks, but investigate their limitations. A clean database does not establish that a package is well maintained or appropriate for the architecture, and a high-severity advisory may be unreachable in your deployment. Record the reasoning and any compensating controls in the review.
How do I choose review depth for different changes?
There is no universal numeric score, so scale the review according to consequences and uncertainty. Five practical axes help: impact if wrong, security sensitivity, breadth of files and interfaces, dependency or configuration changes, and whether tests exercise real behavior or only mocked paths.
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| Lower review intensity | Higher review intensity |
|---|---|
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| No new dependency, permission, data flow, or deployment edit | New package, schema, credential, network call, or workflow permission |
| Strong existing tests that exercise real behavior | Mostly generated tests, heavy mocking, or weak baseline coverage |
| Easy rollback and limited user impact | Irreversible migration, safety-critical path, or sensitive data |
| One or two files with an explainable diff | Unexpected scope, generated files, lockfile churn, or deleted tests |
Higher-risk patches merit a second human reviewer, threat modeling, production-like integration tests, staged rollout, and an explicit rollback plan. Lower-risk patches still require a complete diff and independent checks; “low intensity” never means automatic approval.
How should I record AI assistance and the approval decision?
Keep traceability in the normal commit and pull-request process. State that an agent was used, identify the scope it contributed to, list the checks a human ran, and record exceptions or follow-up work. Do not paste an unreviewed agent transcript as evidence; summarize the verifiable actions and link to logs or test runs that the team can access.
Example pull-request note
AI assistance: implementation and initial tests were generated with an AI coding agent.
Human review: complete diff reviewed, including CI, dependencies, configuration, and tests.
Validation: build, unit, integration, static analysis, and authorization negative cases passed.
Exceptions: end-to-end test for the external provider is deferred to staging (issue #1234).
Decision: approved by [reviewer] after the listed checks.Replace the bracketed reviewer with a real name or account in your system; do not leave template text in a production record. The Home Office standard gives commit markers and pull-request notes as examples of preserving this traceability (Use AI – Engineering Guidance and Standards).
What are common failure modes, and how do I recover?
The summary says files were untouched, but the diff is broad
Pause review, identify every unexpected edit, and ask for a focused patch. Revert unrelated changes or split them into separately reviewed commits so the intended behavior is assessable.
A test was deleted or weakened to make CI pass
Restore the test, reproduce the original failure, and decide whether the implementation or the test conflicts with the requirement. Require an explicit explanation for any changed assertion and add a regression case before approval.
The suite is green but the behavior is wrong
Write a failing test from the acceptance criterion, then fix the implementation and rerun the complete workflow. Examine whether mocks, fixtures, feature flags, or an untested deployment configuration kept the suite from exercising the real path.
A scanner reports a vulnerability the agent says is harmless
Reproduce the data flow and determine reachability, exploitability, and affected versions with a qualified reviewer. Suppress or accept the finding only with a documented rationale, owner, and review date; otherwise fix it or remove the risky dependency.
The agent changed a migration or deployment file after tests passed
Re-run checks that cover the final artifact, inspect rollback and forward-compatibility behavior, and test the deployment in an isolated environment. Never approve based on results from a tree that differs from the one you will ship.
What should I verify immediately before approval?
- The final diff matches the stated acceptance criteria and has no unexplained files.
- Tests, CI configuration, lockfiles, migrations, and deployment changes received the same scrutiny as application code.
- Agent-authored tests contain meaningful assertions, realistic paths, and independent negative or boundary cases.
- Build, test, lint, type, and security checks were run on the final tree, with new findings addressed or documented.
- Authentication, authorization, input validation, sensitive data handling, dependencies, secrets, and error paths match the project’s requirements.
- Security-tool findings were validated by people with suitable expertise.
- The pull request records AI assistance, human review, checks, exceptions, and the approving decision.
- A rollback or mitigation exists for changes whose failure could affect users or data.
Approval is the point at which a human accepts responsibility for the change. If you cannot explain what the code does, why the tests demonstrate the required behavior, and what risks remain, defer approval until you can.
Frequently Asked Questions
Can I merge AI-generated code when every automated check passes?
Not by that fact alone. Passing checks can coexist with an incorrect requirement interpretation, an untested deployment setting, or a test suite that mocks away the defect. Require a human-readable explanation of behavior and risk, plus a complete diff review, before merging.
Should I ask the agent to review its own pull request first?
You may use a second agent pass to find obvious issues, but treat it as an additional static check rather than an independent approval. The same model can repeat the original assumption, so a human must decide whether findings are valid and whether important cases are missing.
What is the safest way to handle an AI-generated database migration?
Test the migration against a copy of production-shaped data, verify forward and rollback behavior, and check compatibility with the application versions that will run during rollout. Keep destructive operations gated and require an explicit backup and recovery plan before release.
How can I tell whether a generated test is over-mocked?
Trace each mock to the boundary it is meant to isolate. If every collaborator, database call, or authorization decision is replaced, the test may prove only that the mock was called; add an integration or contract test that exercises the real interaction and asserts the externally visible result.
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